Classification of perimetric data for supporting glaucoma diagnosisJanja Belinc
, 2018, master's thesis
Abstract: The aim of the study: Glaucoma is a chronic, progressive and asymptomatic retinal disease which results in an irreversible visual field loss. The main objective of this Master’s thesis work was to study the applicability of classification techniques for supporting glaucoma diagnosis.
Research Methodology: In this study perimetric data was obtained by SPARK strategy implemented in Oculus perimeters and provided by medical experts from the Hospital Universitario de Canarias (HUC). This data was used for constructing the feature vectors for the classification problem. Feature vectors of 66 values and feature vectors of 6 values were tested in the experiments. The proposed classification study attempted to: a) demonstrate that the studied classifiers were able to distinguish between “healthy” and “glaucomatous” eyes using only perimetric data, and b) analyse which feature vector design was the most suitable to accomplish this task.
Results: The classification results showed that classifiers performed better on 6 than on 66 perimetry values, which demonstrated the suitability of the 6 points selected by the SPARK strategy and supported its use in medical field.
Conclusion: In this study two remarkable findings for pattern recognition in perimetric data were obtained. Firstly, that reducing the dataset improved the efficiency of the studied classifier, and secondly, that simple pattern recognition models types were more efficient than complex ones.
Keywords: Eye disease, visual field, SPARK perimetry, pattern recognition, machine learning, supervised learning, ROC analysis
Published in DKUM: 27.08.2018; Views: 890; Downloads: 85
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Patterns in symmetry breaking transitionsSamo Kralj
, Robert Repnik
, 2012, original scientific article
Abstract: It is now well accepted that we all have amazing capabilities in recognizing faces in a fraction of a second. This specific pattern recognition ability could be by appropriate training transferred to some other field of expertise. At the same time pattern recognition skills are becoming increasingly important survival strategy in the modern competitive world which faces information overload. In the paper we demonstrate an example of pattern-recognition type of lecturing modern physics. By using already absorbed knowledge and analogies we exploit our innate pattern recognition brain capabilities for more effective learning of new concepts in physics.
Keywords: pattern recognition, universalities, liquid crystals, cosmology
Published in DKUM: 15.12.2017; Views: 1418; Downloads: 119
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The extraction of neural information from the surface EMG for the control of upper-limb prostheses : emerging avenues and challengesDario Farina
, Ning Jiang
, Hubertus Rehbaum
, Aleš Holobar
, Bernhard Graimann
, Hans Dietl
, Oskar Aszmann
, 2014, original scientific article
Abstract: Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.
Keywords: neural drive to muscle, high-density EMG, motor neuron, motor unit, myoelectronic control, pattern recognition, regression
Published in DKUM: 25.05.2015; Views: 1032; Downloads: 0
An efficient chain code with Huffman codingYong Kui Liu
, Borut Žalik
, 2005, original scientific article
Abstract: This paper presents a new chain code based on the eight-direction Freeman code. Each element in the chain is coded as a relative angle difference between it and the previous element. Statistical analysis showed that the probabilities of the Freeman codes differ importantly. Therefore, the Huffman coding was applied. The proposed chain code requires 1.97 bits/code, its chainlength is small, it allows representation of non-closed patterns and is rotationally independent.
Keywords: computer science, pattern recognition, chain code, Huffman code, object representations, chain code compression
Published in DKUM: 01.06.2012; Views: 1579; Downloads: 102
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